Attributes ---------- module_level_variable1 : int Module level variables may be documented in either the ``Attributes`` section of the module docstring, or in an inline docstring immediately following the variable. Either form is acceptable, but the two should not be mixed. Choose one convention to document module level variables and be consistent with it. .. _NumPy docstring standard: https://numpydoc.readthedocs.io/en/latest/format.html#docstring-standard """ module_level_variable1 = 12345 module_level_variable2 = 98765 """int: Module level variable documented inline.

Napoleon is a pre-processor that parses NumPy and Google style docstrings and converts them to reStructuredText before Sphinx attempts to parse them.

This package provides the numpydoc Sphinx extension for handling docstrings formatted according to the NumPy documentation format.

If you have suggestions for improvements, post them on the numpy-discussion list. Our docstring standard uses re-structured text (reST) syntax and is rendered using Sphinx (a pre-processor that understands the particular documentation style we are using).

These are shipped in this numpydoc package, in case you want to make use of them in third-party projects. The numpydoc extension provides support for the Numpy docstring format in Sphinx, and adds the code description directives np:function, np-c:function, etc.

When using Sphinx in combination with the numpy conventions, you should use the numpydoc extension so that your docstrings will be handled correctly. For example, Sphinx will extract the Parameters section from your docstring and convert it into a field list.

To see which functions are available in numpy, type np.<TAB> (where <TAB> refers to the TAB key), or use np.*cos*?<ENTER> (where <ENTER> refers to the ENTER key) to narrow down the list. To view the docstring for a function, use np.cos?<ENTER> (to view the docstring) and np.cos??<ENTER> (to view the source code).

To see which functions are available in numpy, type np.<TAB> (where <TAB> refers to the TAB key), or use np.*cos*?<ENTER> (where <ENTER> refers to the ENTER key) to narrow down the list. To view the docstring for a function, use np.cos?<ENTER> (to view the docstring) and np.cos??<ENTER> (to view the source code).

This page shows how autosummary works with numpydoc and a NumPy-style docstring.

January 31, 2021 - When using Sphinx in combination with the numpy conventions, you should use the numpydoc extension so that your docstrings will be handled correctly. For example, Sphinx will extract the Parameters section from your docstring and convert it into a field list.

February 14, 2025 - In the table below, you can see a comparison of the different types of docstring formats we've mentioned above: Sphinx Style: Often used in large projects where comprehensive documentation is required. It integrates well with Sphinx's documentation generator.

These are shipped in this numpydoc package, in case you want to make use of them in third-party projects. The numpydoc extension provides support for the Numpy docstring format in Sphinx, and adds the code description directives np:function, np-c:function, etc.

Sphinx extension to support docstrings in Numpy format

May 24, 2020 - When using Sphinx in combination with the numpy conventions, you should use the numpydoc extension so that your docstrings will be handled correctly. For example, Sphinx will extract the Parameters section from your docstring and convert it into a field list.

This page shows how autosummary works with numpydoc and a NumPy-style docstring.

Any special member with a docstring will be included in the output, if ``napoleon_include_special_with_doc`` is set to True. This behavior can be enabled by changing the following setting in Sphinx's conf.py:: napoleon_include_special_with_doc = True """ pass def __special_without_docstring__(self): pass def _private(self): """By default private members are not included.

.. [2] `Sphinx <http://sphinx-doc.org/domains.html#domains>`_, Sphinx Domains \ Documentation. Examples -------- >>> data = format_exception_numpy('dumb', 0, IOError) """ return etype, value, tb · Haven’t tried to use np in rst docs, only as autodoc from .py docstrings.

Numpy’s documentation uses several custom extensions to Sphinx. These are shipped in this numpydoc package, in case you want to make use of them in third-party projects. ... numpydoc: support for the Numpy docstring format in Sphinx, and add the code description directives np:function, np-c:function, etc.

This package provides the numpydoc Sphinx extension for handling docstrings formatted according to the NumPy documentation format.